OpenRaiser/Intern-Atlas huggingface dataset:
Massive MIMO Performance - TDD Versus FDD: What Do Measurements Say?
Venue: arXiv cs.IT 2017
Year: 2017

Downlink beamforming in Massive MIMO either relies on uplink pilot measurements - exploiting reciprocity and TDD operation, or on the use of a predetermined grid of beams with user equipments reporting their preferred beams, mostly in FDD operation. Massive MIMO in its originally conceived form uses the first strategy, with uplink pilots, whereas there is currently significant commercial interest in the second, grid-of-beams. It has been analytically shown that in isotropic scattering (independent Rayleigh fading) the first approach outperforms the second. Nevertheless there remains controversy regarding their relative performance in practice. In this contribution, the performances of these two strategies are compared using measured channel data at 2.6 GHz.
Hierarchical Surface Prediction for 3D Object Reconstruction
Venue: arXiv cs.CV 2017
Year: 2017

Recently, Convolutional Neural Networks have shown promising results for 3D geometry prediction. They can make predictions from very little input data such as a single color image. A major limitation of such approaches is that they only predict a coarse resolution voxel grid, which does not capture the surface of the objects well. We propose a general framework, called hierarchical surface prediction (HSP), which facilitates prediction of high resolution voxel grids. The main insight is that it is sufficient to predict high resolution voxels around the predicted surfaces. The exterior and interior of the objects can be represented with coarse resolution voxels. Our approach is not dependent on a specific input type. We show results for geometry prediction from color images, depth images and shape completion from partial voxel grids. Our analysis shows that our high resolution predictions are more accurate than low resolution predictions.
Reprogramming Matter, Life, and Purpose
Venue: arXiv cs.CY 2017
Year: 2017

Reprogramming matter may sound far-fetched, but we have been doing it with increasing power and staggering efficiency for at least 60 years, and for centuries we have been paving the way toward the ultimate reprogrammed fate of the universe, the vessel of all programs. How will we be doing it in 60 years' time and how will it impact life and the purpose both of machines and of humans?
On the idea of a new artificial intelligence based optimization algorithm inspired from the nature of vortex
Venue: arXiv cs.AI 2017
Year: 2017

In this paper, the idea of a new artificial intelligence based optimization algorithm, which is inspired from the nature of vortex, has been provided briefly. As also a bio-inspired computation algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires from some dynamics that are occurred in the sense of vortex nature. Briefly, the algorithm is also a swarm-oriented evolutional problem solution approach; because it includes many methods related to elimination of weak swarm members and trying to improve the solution process by supporting the solution space via new swarm members. In order have better idea about success of the algorithm; it has been tested via some benchmark functions. At this point, the obtained results show that the algorithm can be an alternative to the literature in terms of single-objective optimization solution ways. Vortex Optimization Algorithm (VOA) is the name suggestion by the authors; for this new idea of intelligent optimization approach.
Principal Inertia Components and Applications
Venue: arXiv cs.IT 2017
Year: 2017

We explore properties and applications of the Principal Inertia Components (PICs) between two discrete random variables $X$ and $Y$. The PICs lie in the intersection of information and estimation theory, and provide a fine-grained decomposition of the dependence between $X$ and $Y$. Moreover, the PICs describe which functions of $X$ can or cannot be reliably inferred (in terms of MMSE) given an observation of $Y$. We demonstrate that the PICs play an important role in information theory, and they can be used to characterize information-theoretic limits of certain estimation problems. In privacy settings, we prove that the PICs are related to fundamental limits of perfect privacy.
A History of Metaheuristics
Venue: arXiv cs.AI 2017
Year: 2017

This chapter describes the history of metaheuristics in five distinct periods, starting long before the first use of the term and ending a long time in the future.
Indirect Shared Control of Highly Automated Vehicles for Cooperative Driving between Driver and Automation
Venue: arXiv cs.RO 2017
Year: 2017

It is widely acknowledged that drivers should remain in the control loop of automated vehicles before they completely meet real-world operational conditions. This paper introduces an `indirect shared control' scheme for steer-by-wire vehicles, which allows the vehicle control authority to be continuously shared between the driver and automation through unphysical cooperation. This paper first balances the control objectives of the driver and automation in a weighted summation, and then models the driver's adaptive control behavior using a predictive control approach. The driver adaptation modeling enables off-line evaluations of indirect shared control systems and thus facilitates the design of the assistant controller. Unlike any conventional driver model for manual driving, this model assumes that the driver can learn and incorporate the controller strategy into his internal model for more accurate path following. To satisfy the driving demands in different scenarios, a sliding-window detector is designed to continuously monitor the driver intention and automatically switch the authority weights between the driver and automation. The simulation results illustrate the advantages of considering the driver adaptation in path-following and obstacle-avoidance tasks, and show the effectiveness of indirect shared control for cooperative driving.
Dynamic Rank Maximal Matchings
Venue: arXiv cs.DM 2017
Year: 2017

We consider the problem of matching applicants to posts where applicants have preferences over posts. Thus the input to our problem is a bipartite graph G = (A U P,E), where A denotes a set of applicants, P is a set of posts, and there are ranks on edges which denote the preferences of applicants over posts. A matching M in G is called rank-maximal if it matches the maximum number of applicants to their rank 1 posts, subject to this the maximum number of applicants to their rank 2 posts, and so on. We consider this problem in a dynamic setting, where vertices and edges can be added and deleted at any point. Let n and m be the number of vertices and edges in an instance G, and r be the maximum rank used by any rank-maximal matching in G. We give a simple O(r(m+n))-time algorithm to update an existing rank-maximal matching under each of these changes. When r = o(n), this is faster than recomputing a rank-maximal matching completely using a known algorithm like that of Irving et al., which takes time O(min((r + n, r*sqrt(n))m).
MIMO Underwater Visible Light Communications: Comprehensive Channel Study, Performance Analysis, and Multiple-Symbol Detection
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we analytically study the bit error rate (BER) performance of underwater visible light communication (UVLC) systems with binary pulse position modulation (BPPM). We simulate the channel fading-free impulse response (FFIR) based on Monte Carlo numerical method to take into account the absorption and scattering effects. Additionally, to characterize turbulence effects, we multiply the aforementioned FFIR by a fading coefficient which for weak oceanic turbulence can be modeled as a lognormal random variable (RV). Moreover, to mitigate turbulence effects, we employ multiple transmitters and/or receivers, i.e., spatial diversity technique over UVLC links. Closed-form expressions for the system BER are provided, when equal gain combiner (EGC) is employed at the receiver side, thanks to Gauss-Hermite quadrature formula and approximation to the sum of lognormal RVs. We further apply saddle-point approximation, an accurate photon-counting-based method, to evaluate the system BER in the presence of shot noise. Both laser-based collimated and light emitting diode (LED)-based diffusive links are investigated. Since multiple-scattering effect of UVLC channels on the propagating photons causes considerable inter-symbol interference (ISI), especially for diffusive channels, we also obtain the optimum multiple-symbol detection (MSD) algorithm to significantly alleviate ISI effects and improve the system performance. Our numerical analysis indicates good matches between the analytical and photon-counting results implying the negligibility of signal-dependent shot noise, and also between analytical results and numerical simulations confirming the accuracy of our derived closed-form expressions for the system BER. Besides, our results show that spatial diversity significantly mitigates fading impairments while MSD considerably alleviates ISI deteriorations.
Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies/brands. More specifically, our approach leverages affective lexica and word embeddings in combination with convolutional neural networks to infer the sentiment of financial news headlines towards a target company. Such architecture was used and evaluated in the context of the SemEval 2017 challenge (task 5, subtask 2), in which it obtained the best performance.
A Decision Support Method for Recommending Degrees of Exploration in Exploratory Testing
Venue: arXiv cs.SE 2017
Year: 2017

Exploratory testing is neither black nor white, but rather a continuum of exploration exists. In this research we propose an approach for decision support helping practitioners to distribute time between different degrees of exploratory testing on that continuum. To make the continuum manageable, five levels have been defined: freestyle testing, high, medium and low degrees of exploration, and scripted testing. The decision support approach is based on the repertory grid technique. The approach has been used in one company. The method for data collection was focus groups. The results showed that the proposed approach aids practitioners in the reflection of what exploratory testing levels to use, and aligns their understanding for priorities of decision criteria and the performance of exploratory testing levels in their contexts. The findings also showed that the participating company, which is currently conducting mostly scripted testing, should spend more time on testing using higher degrees of exploration in comparison to scripted testing.
On the Combinatorial Power of the Weisfeiler-Lehman Algorithm
Venue: arXiv cs.DS 2017
Year: 2017

The classical Weisfeiler-Lehman method WL[2] uses edge colors to produce a powerful graph invariant. It is at least as powerful in its ability to distinguish non-isomorphic graphs as the most prominent algebraic graph invariants. It determines not only the spectrum of a graph, and the angles between standard basis vectors and the eigenspaces, but even the angles between projections of standard basis vectors into the eigenspaces. Here, we investigate the combinatorial power of WL[2]. For sufficiently large k, WL[k] determines all combinatorial properties of a graph. Many traditionally used combinatorial invariants are determined by WL[k] for small k. We focus on two fundamental invariants, the num- ber of cycles Cp of length p, and the number of cliques Kp of size p. We show that WL[2] determines the number of cycles of lengths up to 6, but not those of length 8. Also, WL[2] does not determine the number of 4-cliques.
Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods
Venue: arXiv cs.LG 2017
Year: 2017

The problem of Non-Gaussian Component Analysis (NGCA) is about finding a maximal low-dimensional subspace $E$ in $\mathbb{R}^n$ so that data points projected onto $E$ follow a non-gaussian distribution. Although this is an appropriate model for some real world data analysis problems, there has been little progress on this problem over the last decade. In this paper, we attempt to address this state of affairs in two ways. First, we give a new characterization of standard gaussian distributions in high-dimensions, which lead to effective tests for non-gaussianness. Second, we propose a simple algorithm, \emph{Reweighted PCA}, as a method for solving the NGCA problem. We prove that for a general unknown non-gaussian distribution, this algorithm recovers at least one direction in $E$, with sample and time complexity depending polynomially on the dimension of the ambient space. We conjecture that the algorithm actually recovers the entire $E$.
Charting the replica symmetric phase
Venue: arXiv cs.DM 2017
Year: 2017

Diluted mean-field models are spin systems whose geometry of interactions is induced by a sparse random graph or hypergraph. Such models play an eminent role in the statistical mechanics of disordered systems as well as in combinatorics and computer science. In a path-breaking paper based on the non-rigorous `cavity method', physicists predicted not only the existence of a replica symmetry breaking phase transition in such models but also sketched a detailed picture of the evolution of the Gibbs measure within the replica symmetric phase and its impact on important problems in combinatorics, computer science and physics [Krzakala et al.: PNAS 2007]. In this paper we rigorise this picture completely for a broad class of models, encompassing the Potts antiferromagnet on the random graph, the $k$-XORSAT model and the diluted $k$-spin model for even $k$. We also prove a conjecture about the detection problem in the stochastic block model that has received considerable attention [Decelle et al.: Phys. Rev. E 2011].
Computing top-k Closeness Centrality Faster in Unweighted Graphs
Venue: arXiv cs.DS 2017
Year: 2017

Given a connected graph $G=(V,E)$, the closeness centrality of a vertex $v$ is defined as $\frac{n-1}{\sum_{w \in V} d(v,w)}$. This measure is widely used in the analysis of real-world complex networks, and the problem of selecting the $k$ most central vertices has been deeply analysed in the last decade. However, this problem is computationally not easy, especially for large networks: in the first part of the paper, we prove that it is not solvable in time $Ø(|E|^{2-ε})$ on directed graphs, for any constant $ε>0$, under reasonable complexity assumptions. Furthermore, we propose a new algorithm for selecting the $k$ most central nodes in a graph: we experimentally show that this algorithm improves significantly both the textbook algorithm, which is based on computing the distance between all pairs of vertices, and the state of the art. For example, we are able to compute the top $k$ nodes in few dozens of seconds in real-world networks with millions of nodes and edges. Finally, as a case study, we compute the $10$ most central actors in the IMDB collaboration network, where two actors are linked if they played together in a movie, and in the Wikipedia citation network, which contains a directed edge from a page $p$ to a page $q$ if $p$ contains a link to $q$.
Rethinking the Generational Gap in Online News Use: An Infrastructural Perspective
Venue: arXiv cs.CY 2017
Year: 2017

Our study investigates the role of infrastructures in shaping online news usage by contrasting use patterns of two social groups,millennials and boomers,that are specifically located in news infrastructures. Typically based on self reported data, popular press and academics tend to highlight the generational gap in news usage and link it to divergence in values and preferences of the two age cohorts. In contrast, we conduct relational analyses of shared usage obtained from passively metered usage data across a vast range of online news outlets for millennials and boomers. We compare each cohort's usage networks comprising various types of news websites. Our analyses reveal a smaller than commonly assumed generational gap in online news usage, with characteristics that manifest the multifarious effects of the infrastructural aspect of the media environment, alongside those of preferences.
A Computational Approach to Relative Aesthetics
Venue: arXiv cs.CV 2017
Year: 2017

Computational visual aesthetics has recently become an active research area. Existing state-of-art methods formulate this as a binary classification task where a given image is predicted to be beautiful or not. In many applications such as image retrieval and enhancement, it is more important to rank images based on their aesthetic quality instead of binary-categorizing them. Furthermore, in such applications, it may be possible that all images belong to the same category. Hence determining the aesthetic ranking of the images is more appropriate. To this end, we formulate a novel problem of ranking images with respect to their aesthetic quality. We construct a new dataset of image pairs with relative labels by carefully selecting images from the popular AVA dataset. Unlike in aesthetics classification, there is no single threshold which would determine the ranking order of the images across our entire dataset. We propose a deep neural network based approach that is trained on image pairs by incorporating principles from relative learning. Results show that such relative training procedure allows our network to rank the images with a higher accuracy than a state-of-art network trained on the same set of images using binary labels.
Investigating Human Factors in Image Forgery Detection
Venue: arXiv cs.CV 2017
Year: 2017

In today's age of internet and social media, one can find an enormous volume of forged images on-line. These images have been used in the past to convey falsified information and achieve harmful intentions. The spread and the effect of the social media only makes this problem more severe. While creating forged images has become easier due to software advancements, there is no automated algorithm which can reliably detect forgery. Image forgery detection can be seen as a subset of image understanding problem. Human performance is still the gold-standard for these type of problems when compared to existing state-of-art automated algorithms. We conduct a subjective evaluation test with the aid of eye-tracker to investigate into human factors associated with this problem. We compare the performance of an automated algorithm and humans for forgery detection problem. We also develop an algorithm which uses the data from the evaluation test to predict the difficulty-level of an image (the difficulty-level of an image here denotes how difficult it is for humans to detect forgery in an image. Terms such as "Easy/difficult image" will be used in the same context). The experimental results presented in this paper should facilitate development of better algorithms in the future.
Classification of Diabetic Retinopathy Images Using Multi-Class Multiple-Instance Learning Based on Color Correlogram Features
Venue: arXiv cs.CV 2017
Year: 2017

All people with diabetes have the risk of developing diabetic retinopathy (DR), a vision-threatening complication. Early detection and timely treatment can reduce the occurrence of blindness due to DR. Computer-aided diagnosis has the potential benefit of improving the accuracy and speed in DR detection. This study is concerned with automatic classification of images with microaneurysm (MA) and neovascularization (NV), two important DR clinical findings. Together with normal images, this presents a 3-class classification problem. We propose a modified color auto-correlogram feature (AutoCC) with low dimensionality that is spectrally tuned towards DR images. Recognizing the fact that the images with or without MA or NV are generally different only in small, localized regions, we propose to employ a multi-class, multiple-instance learning framework for performing the classification task using the proposed feature. Extensive experiments including comparison with a few state-of-art image classification approaches have been performed and the results suggest that the proposed approach is promising as it outperforms other methods by a large margin.
Optimal trade-offs for pattern matching with $k$ mismatches
Venue: arXiv cs.DS 2017
Year: 2017

Given a pattern of length $m$ and a text of length $n$, the goal in $k$-mismatch pattern matching is to compute, for every $m$-substring of the text, the exact Hamming distance to the pattern or report that it exceeds $k$. This can be solved in either $\widetilde{O}(n \sqrt{k})$ time as shown by Amir et al. [J. Algorithms 2004] or $\widetilde{O}((m + k^2) \cdot n/m)$ time due to a result of Clifford et al. [SODA 2016]. We provide a smooth time trade-off between these two bounds by designing an algorithm working in time $\widetilde{O}( (m + k \sqrt{m}) \cdot n/m)$. We complement this with a matching conditional lower bound, showing that a significantly faster combinatorial algorithm is not possible, unless the combinatorial matrix multiplication conjecture fails.
Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF
Venue: arXiv cs.CL 2017
Year: 2017

We present a character-based model for joint segmentation and POS tagging for Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is adapted and applied with novel vector representations of Chinese characters that capture rich contextual information and lower-than-character level features. The proposed model is extensively evaluated and compared with a state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The experimental results indicate that our model is accurate and robust across datasets in different sizes, genres and annotation schemes. We obtain state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint segmentation and POS tagging.
On the Relation between Color Image Denoising and Classification
Venue: arXiv cs.CV 2017
Year: 2017

Large amount of image denoising literature focuses on single channel images and often experimentally validates the proposed methods on tens of images at most. In this paper, we investigate the interaction between denoising and classification on large scale dataset. Inspired by classification models, we propose a novel deep learning architecture for color (multichannel) image denoising and report on thousands of images from ImageNet dataset as well as commonly used imagery. We study the importance of (sufficient) training data, how semantic class information can be traded for improved denoising results. As a result, our method greatly improves PSNR performance by 0.34 - 0.51 dB on average over state-of-the art methods on large scale dataset. We conclude that it is beneficial to incorporate in classification models. On the other hand, we also study how noise affect classification performance. In the end, we come to a number of interesting conclusions, some being counter-intuitive.
Seymour's second neighbourhood conjecture for quasi-transitive oriented graphs
Venue: arXiv cs.DM 2017
Year: 2017

Seymour's second neighbourhood conjecture asserts that every oriented graph has a vertex whose second out-neighbourhood is at least as large as its out-neighbourhood. In this paper, we prove that the conjecture holds for quasi-transitive oriented graphs, which is a superclass of tournaments and transitive acyclic digraphs. A digraph $D$ is called quasi-transitive is for every pair $xy,yz$ of arcs between distinct vertices $x,y,z$, $xz$ or $zx$ ("or" is inclusive here) is in $D$.